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3 Secrets to Effective Self-Service Data Preparation Governance

The self-service analytics explosion was supposed to make businesses more agile, analytical and confident in their decisions. Instead, this newfound agility and autonomy has led to confusion and a lack of accountability – and most importantly a loss of control over data usage. With everyone working in silos, there’s a developing mistrust of data and analytics outcomes and most organizations are more vulnerable to data governance, security, regulatory, compliance, and privacy gaps. But what if the idea that agility and governance can’t co-exist is just a myth?

There’s a major shift in how businesses are approaching analytics – moving from self-service to team-based, enterprise data preparation and analytics. And this revolution will be led by Monarch Swarm. With collaboration, socialization and governance as its core principles, team-based, enterprise data preparation and analytics will create a data-driven culture by bringing analysts together. Teams will be able to work together – rather than in silos – to create, find, access, validate and share governed, trustworthy data sets and models for true enterprise collaboration.

Jen Underwood, industry expert and founder of Impact Analytix discusses how to mitigate common governance risks and provide practical considerations for different types of analytical environments.